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Speech Research and Corpora in Thailand

Speech Research and Corpora in Thailand. Virach Sornlertlamvanich Information Research and Development Division National Electronics and Computer Technology Center (NECTEC) THAILAND. Oriental COCOSDA Workshop 2000, Oct. 16, 2000, Beijing, China. Introduction to Thai (1): Morphology.

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Speech Research and Corpora in Thailand

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  1. Speech Research and Corpora in Thailand Virach SornlertlamvanichInformation Research and Development DivisionNational Electronics and Computer Technology Center (NECTEC) THAILAND Oriental COCOSDA Workshop 2000, Oct. 16, 2000, Beijing, China.

  2. Introduction to Thai (1): Morphology • Running text (a paragraph): สวัสดีครับ ผมชื่อวิรัช ศรเลิศล้ำวาณิช ปัจจุบันเป็นผู้อำนวยการฝ่ายวิจัยและพัฒนาสาขาสารสนเทศ ศูนย์เทคโนโลยีอิเล็กทรอนิกส์และคอมพิวเตอร์แห่งชาติ ผมเริ่มสนใจงานวิจัยในสาขาการประมวลผลภาษาธรรมชาติตั้งแต่ที่ได้มีโอกาสเข้าร่วมโครงการวิจัยและพัฒนาระบบแปลภาษาในปี 1989 • Writing in 4 levels • No. of characters (signs) 46 consonants; 18 vowels; 4 tones; 9 symbols; 10 digits • No word boundary Ex: “GODISNOWHERE” 1) God is nowhere 2) God is now here3) God is no where vowel tone consonant vowel

  3. Introduction to Thai (2): Syntax • No explicit sentence marker- space character for pausing • Sentence pattern- (S) (V) (O) Ex: ฉัน เห็น เขา(I) (saw) (him) • No inflection forms- tenses use adverbs and auxiliary verbs- plural or singular nounsuse quantifiers, classifiers or determiners- subject-verb agreements • No syntactic marker- word position

  4. Introduction to Thai (3): Phonology • A Thai syllable (sounds):/ C(C) V(V) C T / tonal level (5) initial consonant (33) final consonant (8) vowel (24) • Different tones convey different meanings /su:aj4/ = beautiful /su:aj0/ = terrible •No liaison:A word has the same pronunciation, no matter where it is. •Linking syllable pronunciation:ตุ๊กแก (gecko) = tuk4 - kae -> ตุ๊ก = tuk4 ตุ๊กตา (doll) = tuk4 - ka1 - ta0 -> ตุ๊ก = tuk4 - ka1 (grapheme to phoneme conversion)

  5. Introduction to Thai (4): Summary • Simple grammar- easy for generation- hard for analysis and recognition • Sharable problems among Asian languages- word segmentation- indexing for IR- lexical acquisition- tone recognition and generation

  6. 3 4 0 2 1 Thai Tones Research on Speech (1):Recognition • Tone recognition Current state - Object: Syllable-segmented speech - Feature: Energy, Zero-crossing, F0 - Method: Neural net, Analysis-by-synthesis Ongoing - Continuous speech • Syllable detection Current state - Object: Connected speech - Feature: Energy, Zero-crossing, Duration Ongoing - Continuous speech

  7. Research on Speech (2):Recognition • Isolated word-based recognition Current state - Mel-frequency cepstrum (MFC) - Neural net, Fuzzy, HMM Ongoing - Applications (digits, commands) • Large vocabulary continuous speech recognition (LVCSR) Current state - Isolated phoneme recognition - Preparing basic tools for CSR Ongoing - Creating LVCSR corpus

  8. Research on Speech (3):Synthesis • Text analysis Current state - Word / Phrase / Sentence segmentation by POS tagging model, Rule, Machine learning - Letter-to-sound: Rules and Pronunciation dictionary Ongoing - Letter-to-sound: PGLR parser (87-94%) • Speech synthesis Current state - Demisyllable-concatenation based - LSP-based spectral smoothing- Duration adjustment- F0 contour smoothing Ongoing - Smoothing, Statistical prosody analysis

  9. Research on Speech (4):Synthesis • LSP parameter smoothing ยา /ja:/ /ja/ /a:/

  10. Research on Speech (5):Speaker Recognition • Speaker identification (SID) Current state - Text-dependent, Closed speaker set, Office environment speech - Dynamic time warping (DTW; 90-97%), Gaussian mixture model (GMM; 92-98%) Ongoing - Telephony environment speech • Speaker verification (SV) - Ongoing

  11. Thai Speech Corpora (1) •Current state: - A number of separated speech corpora e.g. Speech database of Thai digits 0-9 for SID Speech database of Thai polysyllabic words •Ongoing: - LVCSR corpus for Speech dictation system up to 5,000 vocabulary size with Phonetically-balanced set - Prosody tagging speech corpus for statistical prosody analysis in improving synthesis system

  12. Thai Speech Corpora (2) •Basic tools required: Dictionary - Manually coding - Corpus-based extraction Word segmentation - Longest matching (92%) - Maximal matching (93%) - POS N-gram (96%) - Machine learning (97%) Sentence extraction - POS N-gram (85%) - Machine learning (89%)

  13. Thai Speech Corpora (3) •Basic tools required: Letter-to-sound - Rule-based and dictionary - PGLR parser (87%-94%) Basic tagged corpus - ORDHID: POS tagging corpus160 documents; 5.75 MB; 311,426 words Other tools - Automatic sentence selection for phonetically balanced set - Automatic phoneme labeling

  14. Thai Text to Speech: Demo สวัสดีครับ ผมชื่อวิรัช ศรเลิศล้ำวาณิช ปัจจุบันเป็นผู้อำนวยการฝ่ายวิจัยและพัฒนาสาขาสารสนเทศ ศูนย์เทคโนโลยีอิเล็กทรอนิกส์และคอมพิวเตอร์แห่งชาติ ผมเริ่มสนใจงานวิจัยในสาขาการประมวลผลภาษาธรรมชาติตั้งแต่ที่ได้มีโอกาสเข้าร่วมโครงการวิจัยและพัฒนาระบบแปลภาษาในปี 1989 Hello, I am Virach Sornlertlamvanich, the director of Information Research and Development Division, National Electronics and Computer Technology Center. I began to interest myself in the research of Natural Language Processing since having a chance in participating in the Machine Translation Research and Development project in 1989.

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